| Steel and Composite Structures Volume 29, Number 3, November10 2018, pages 309-318 DOI: http://dx.doi.org/10.12989/scs.2018.29.3.309 | |
Patch load resistance of longitudinally stiffened webs: Modeling via support vector machines
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Ahmet Emin Kurtoglu
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| Abstract [Full Text] | |
| Steel girders are the structural members often used for passing long spans. Mostly being subjected to patch loading, or concentrated loading, steel girders are likely to face sudden deformation or damage e.g., web breathing. Horizontal or vertical stiffeners are employed to overcome this phenomenon. This study aims at assessing the feasibility of a machine learning method, namely the support vector machines (SVM) in predicting the patch loading resistance of longitudinally stiffened webs. A database consisting of 162 test data is utilized to develop SVM models and the model with best performance is selected for further inspection. Existing formulations proposed by other researchers are also investigated for comparison. BS5400 and other existing models (model I, model II and model III) appear to yield underestimated predictions with a large scatter; i.e., mean experimental-to-predicted ratios of 1.517, 1.092, 1.155 and 1.256, respectively; whereas the selected SVM model has high prediction accuracy with significantly less scatter. Robust nature and accurate predictions of SVM confirms its feasibility of potential use in solving complex engineering problems. | |
| Key Words | |
| steel girders; patch loading; longitudinal stiffener; support vector machines; machine learning | |
| Address | |
| Department of Civil Engineering, Istanbul Gelisim University, Cihangir Mah. Şht. P. Onb. Murat Şengöz Sok. No: 8 34310 Avc | |
